At-home wearables and machine learning capture motor impairment and progression in adult ataxias.

ABSTRACT: A significant barrier to developing disease-modifying therapies for spinocerebellar ataxias (SCAs) and multiple system atrophy of the cerebellar type (MSA-C) is the scarcity of tools to sensitively measure disease progression in clinical trials. Wearable sensors worn continuously during natural behavior at home have the potential to produce ecologically valid and precise measures of motor function by leveraging frequent and numerous high-resolution samples of behavior. Here we test whether movement-building block characteristics (i.e., submovements), obtained from the wrist and ankle during natural behavior at home, can sensitively capture disease progression in SCAs and MSA-C, as recently shown in amyotrophic lateral sclerosis (ALS) and ataxia telangiectasia (A-T). Remotely collected cros

SUBMITTER: Manohar R 

PROVIDER: S-EPMC11581084 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature